Preparation and Curation of Multiyear, Multilocation, Multitrait Datasets.
Adjusted phenotype per trial
Analysis of residuals
Combined phenotype across trials
Descriptive statistics
Design diagnostics
Experimental design
Genotype × environment
Genotype–phenotype association
Linear mixed model
Multienvironment trials
Outliers
Raw phenotype per trial
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
31
5
2022
pubmed:
1
6
2022
medline:
3
6
2022
Statut:
ppublish
Résumé
Genome-wide association studies (GWAS) are a powerful approach to dissect genotype-phenotype associations and identify causative regions. However, this power is highly influenced by the accuracy of the phenotypic data. To obtain accurate phenotypic values, the phenotyping should be achieved through multienvironment trials (METs). In order to avoid any technical errors, the required time needs to be spent on exploring, understanding, curating and adjusting the phenotypic data in each trial before combining them using an appropriate linear mixed model (LMM). The LMM is chosen to minimize as much as possible any effect that can lead to misestimation of the phenotypic values. The purpose of this chapter is to explain a series of important steps to explore and analyze data from METs used to characterize an association panel. Two datasets are used to illustrate two different scenarios.
Identifiants
pubmed: 35641760
doi: 10.1007/978-1-0716-2237-7_6
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
83-104Informations de copyright
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
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